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Record W7108442233 · doi:10.53555/kk0jm854

Decline Of Wild Edible Fruits In Maharashtra’s Tribal Markets: Evidence From Weekly Haat Records 1990-2023

2023· article· W7108442233 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Language
FieldSocial Sciences
TopicSocial and Economic Development in India
Canadian institutionsnot available
Fundersnot available
KeywordsMonocultureTonneDeciduousEarningsTemperate climateNatural forest

Abstract

fetched live from OpenAlex

Weekly haat registers maintained by the Integrated Tribal Development Projects in six major tribal districts of Maharashtra (Nandurbar, Dhule, Nashik, Palghar, Gadchiroli, and Gondia) provide the first systematic, long-term evidence of the collapse of wild edible fruit supply between 1990-91 and 2021-22. Total quantity sold through these official markets has declined from an average of 9 842 tonnes per year in 1990-95 to 2617 tonnes in 2017-22, a fall of 73.4 percent. The number of fruit species regularly appearing in the registers has dropped from 29 to only 9 per season. Among major species, mahua flowers have fallen from 4 126 tonnes to 1 214 tonnes, charoli seeds from 1 348 tonnes to 178 tonnes, amla from 1017 tonnes to 298 tonnes, and jamun from 782 tonnes to 152 tonnes. Real prices paid to collectors (deflated to constant 2022 rupees) have risen between 567 percent (mahua) and 1102 percent (charoli), driven by growing urban and herbal-industry demand, yet actual household cash earnings have declined sharply because collectors now harvest far smaller volumes. The primary drivers are the large-scale replacement of mixed deciduous forests by teak monoculture since the late 1980s, near-total failure of natural regeneration due to uncontrolled grazing and loss of seed-dispersing wildlife, and recent fragmentation caused by highways and mining. The paper concludes that without urgent measures (protection of key species, inclusion of fruits under the Minimum Support Price scheme, and allocation of degraded reserve forest for community-led regeneration), wild edible fruits will soon vanish from both adivasi diets and local economies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0010.003
Scholarly communication0.0010.004
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.256
GPT teacher head0.331
Teacher spread0.075 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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